End of training
Browse files- README.md +109 -109
- model.safetensors +1 -1
- train.ipynb +0 -0
- training_args.bin +1 -1
README.md
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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- name: F1
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type: f1
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- name: Precision
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type: precision
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value: 0.
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- name: Recall
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type: recall
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value: 0.
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the stanford-dogs dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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### Framework versions
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9480077745383868
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- name: F1
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type: f1
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+
value: 0.9459908448725385
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- name: Precision
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type: precision
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value: 0.949860554097917
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- name: Recall
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type: recall
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value: 0.9463076869851217
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| 38 |
---
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| 39 |
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| 40 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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|
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This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the stanford-dogs dataset.
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It achieves the following results on the evaluation set:
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+
- Loss: 0.1854
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+
- Accuracy: 0.9480
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- F1: 0.9460
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| 50 |
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- Precision: 0.9499
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- Recall: 0.9463
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 4.7451 | 0.0777 | 10 | 4.6183 | 0.0717 | 0.0618 | 0.0669 | 0.0681 |
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| 4.5204 | 0.1553 | 20 | 4.3242 | 0.2024 | 0.1493 | 0.1858 | 0.1827 |
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| 4.2163 | 0.2330 | 30 | 3.8514 | 0.3817 | 0.3108 | 0.3755 | 0.3598 |
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| 3.5996 | 0.3107 | 40 | 2.9936 | 0.6025 | 0.5397 | 0.5985 | 0.5852 |
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| 2.7565 | 0.3883 | 50 | 1.8902 | 0.7738 | 0.7419 | 0.8002 | 0.7599 |
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| 1.9695 | 0.4660 | 60 | 1.2027 | 0.8644 | 0.8512 | 0.8810 | 0.8585 |
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| 1.4292 | 0.5437 | 70 | 0.8375 | 0.8902 | 0.8768 | 0.9034 | 0.8853 |
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| 1.1191 | 0.6214 | 80 | 0.5400 | 0.9140 | 0.9085 | 0.9209 | 0.9114 |
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| 0.9249 | 0.6990 | 90 | 0.4183 | 0.9193 | 0.9136 | 0.9284 | 0.9169 |
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| 0.7701 | 0.7767 | 100 | 0.3423 | 0.9237 | 0.9167 | 0.9265 | 0.9207 |
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| 0.7036 | 0.8544 | 110 | 0.3141 | 0.9259 | 0.9199 | 0.9270 | 0.9228 |
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| 0.7279 | 0.9320 | 120 | 0.2814 | 0.9261 | 0.9200 | 0.9301 | 0.9235 |
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| 0.6732 | 1.0097 | 130 | 0.2583 | 0.9278 | 0.9258 | 0.9337 | 0.9264 |
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| 0.5251 | 1.0874 | 140 | 0.2433 | 0.9388 | 0.9343 | 0.9400 | 0.9365 |
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| 0.506 | 1.1650 | 150 | 0.2486 | 0.9293 | 0.9237 | 0.9393 | 0.9284 |
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| 0.4941 | 1.2427 | 160 | 0.2489 | 0.9295 | 0.9276 | 0.9340 | 0.9276 |
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| 0.493 | 1.3204 | 170 | 0.2256 | 0.9361 | 0.9337 | 0.9402 | 0.9344 |
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| 0.4975 | 1.3981 | 180 | 0.2236 | 0.9390 | 0.9352 | 0.9430 | 0.9377 |
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| 0.4742 | 1.4757 | 190 | 0.2291 | 0.9390 | 0.9349 | 0.9443 | 0.9368 |
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| 0.4788 | 1.5534 | 200 | 0.2187 | 0.9385 | 0.9348 | 0.9429 | 0.9359 |
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| 0.4817 | 1.6311 | 210 | 0.2194 | 0.9383 | 0.9366 | 0.9438 | 0.9370 |
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| 0.425 | 1.7087 | 220 | 0.2145 | 0.9395 | 0.9365 | 0.9419 | 0.9374 |
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| 0.4392 | 1.7864 | 230 | 0.2106 | 0.9405 | 0.9367 | 0.9473 | 0.9390 |
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| 0.4295 | 1.8641 | 240 | 0.2031 | 0.9427 | 0.9415 | 0.9461 | 0.9419 |
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| 0.447 | 1.9417 | 250 | 0.2073 | 0.9373 | 0.9341 | 0.9406 | 0.9355 |
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| 0.4718 | 2.0194 | 260 | 0.2073 | 0.9417 | 0.9398 | 0.9436 | 0.9396 |
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| 0.4528 | 2.0971 | 270 | 0.2011 | 0.9427 | 0.9403 | 0.9447 | 0.9401 |
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| 0.3958 | 2.1748 | 280 | 0.1979 | 0.9439 | 0.9402 | 0.9467 | 0.9418 |
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| 0.4325 | 2.2524 | 290 | 0.1993 | 0.9422 | 0.9396 | 0.9448 | 0.9404 |
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| 0.3228 | 2.3301 | 300 | 0.2025 | 0.9397 | 0.9372 | 0.9415 | 0.9375 |
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| 0.383 | 2.4078 | 310 | 0.2032 | 0.9424 | 0.9396 | 0.9471 | 0.9407 |
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| 0.4147 | 2.4854 | 320 | 0.1975 | 0.9434 | 0.9401 | 0.9466 | 0.9418 |
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| 0.3587 | 2.5631 | 330 | 0.2048 | 0.9429 | 0.9412 | 0.9453 | 0.9415 |
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| 0.3481 | 2.6408 | 340 | 0.2110 | 0.9417 | 0.9409 | 0.9453 | 0.9414 |
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| 0.4007 | 2.7184 | 350 | 0.1945 | 0.9448 | 0.9415 | 0.9470 | 0.9429 |
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| 0.3719 | 2.7961 | 360 | 0.2025 | 0.9414 | 0.9404 | 0.9447 | 0.9408 |
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| 0.3993 | 2.8738 | 370 | 0.2012 | 0.9448 | 0.9419 | 0.9485 | 0.9430 |
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| 0.3745 | 2.9515 | 380 | 0.1924 | 0.9451 | 0.9415 | 0.9499 | 0.9435 |
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| 0.3638 | 3.0291 | 390 | 0.1940 | 0.9444 | 0.9424 | 0.9478 | 0.9424 |
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| 0.3421 | 3.1068 | 400 | 0.1897 | 0.9466 | 0.9441 | 0.9496 | 0.9446 |
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| 0.2906 | 3.1845 | 410 | 0.1893 | 0.9470 | 0.9457 | 0.9494 | 0.9457 |
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| 0.3455 | 3.2621 | 420 | 0.1802 | 0.9485 | 0.9471 | 0.9499 | 0.9475 |
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| 0.3338 | 3.3398 | 430 | 0.1926 | 0.9441 | 0.9414 | 0.9473 | 0.9424 |
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| 0.3307 | 3.4175 | 440 | 0.2020 | 0.9419 | 0.9407 | 0.9447 | 0.9409 |
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| 0.367 | 3.4951 | 450 | 0.1934 | 0.9466 | 0.9452 | 0.9487 | 0.9454 |
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| 0.3248 | 3.5728 | 460 | 0.2004 | 0.9419 | 0.9393 | 0.9443 | 0.9401 |
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| 0.3366 | 3.6505 | 470 | 0.1924 | 0.9431 | 0.9410 | 0.9467 | 0.9415 |
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| 0.3342 | 3.7282 | 480 | 0.1938 | 0.9453 | 0.9436 | 0.9468 | 0.9438 |
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| 0.3386 | 3.8058 | 490 | 0.2018 | 0.9444 | 0.9428 | 0.9469 | 0.9430 |
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| 0.3841 | 3.8835 | 500 | 0.1933 | 0.9434 | 0.9414 | 0.9458 | 0.9418 |
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| 0.3174 | 3.9612 | 510 | 0.1902 | 0.9453 | 0.9438 | 0.9466 | 0.9436 |
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| 0.2996 | 4.0388 | 520 | 0.1888 | 0.9466 | 0.9454 | 0.9497 | 0.9460 |
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| 0.2879 | 4.1165 | 530 | 0.1885 | 0.9441 | 0.9428 | 0.9464 | 0.9428 |
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| 0.3035 | 4.1942 | 540 | 0.1909 | 0.9453 | 0.9434 | 0.9475 | 0.9437 |
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| 0.2574 | 4.2718 | 550 | 0.1886 | 0.9453 | 0.9427 | 0.9476 | 0.9438 |
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| 0.3219 | 4.3495 | 560 | 0.1889 | 0.9434 | 0.9411 | 0.9462 | 0.9417 |
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| 0.2827 | 4.4272 | 570 | 0.1896 | 0.9448 | 0.9435 | 0.9464 | 0.9434 |
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| 0.2869 | 4.5049 | 580 | 0.1946 | 0.9444 | 0.9430 | 0.9459 | 0.9427 |
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| 0.3442 | 4.5825 | 590 | 0.1871 | 0.9458 | 0.9444 | 0.9477 | 0.9445 |
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| 0.2739 | 4.6602 | 600 | 0.1881 | 0.9441 | 0.9415 | 0.9470 | 0.9421 |
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| 0.3067 | 4.7379 | 610 | 0.1925 | 0.9475 | 0.9456 | 0.9499 | 0.9456 |
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| 0.2674 | 4.8155 | 620 | 0.1919 | 0.9429 | 0.9405 | 0.9458 | 0.9408 |
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| 0.3029 | 4.8932 | 630 | 0.1870 | 0.9446 | 0.9420 | 0.9468 | 0.9425 |
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| 0.293 | 4.9709 | 640 | 0.1914 | 0.9422 | 0.9398 | 0.9444 | 0.9402 |
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| 0.3242 | 5.0485 | 650 | 0.1906 | 0.9444 | 0.9428 | 0.9463 | 0.9429 |
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| 0.3302 | 5.1262 | 660 | 0.1893 | 0.9453 | 0.9437 | 0.9467 | 0.9439 |
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| 0.2754 | 5.2039 | 670 | 0.1859 | 0.9470 | 0.9452 | 0.9489 | 0.9453 |
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| 0.2794 | 5.2816 | 680 | 0.1876 | 0.9458 | 0.9441 | 0.9473 | 0.9442 |
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| 0.3015 | 5.3592 | 690 | 0.1870 | 0.9463 | 0.9450 | 0.9481 | 0.9451 |
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| 0.2741 | 5.4369 | 700 | 0.1891 | 0.9427 | 0.9415 | 0.9447 | 0.9414 |
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| 0.2856 | 5.5146 | 710 | 0.1898 | 0.9456 | 0.9439 | 0.9470 | 0.9439 |
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| 0.2869 | 5.5922 | 720 | 0.1900 | 0.9463 | 0.9449 | 0.9485 | 0.9448 |
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| 0.2874 | 5.6699 | 730 | 0.1926 | 0.9458 | 0.9434 | 0.9489 | 0.9439 |
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| 0.1988 | 5.7476 | 740 | 0.1883 | 0.9453 | 0.9427 | 0.9469 | 0.9433 |
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| 0.2644 | 5.8252 | 750 | 0.1895 | 0.9473 | 0.9448 | 0.9494 | 0.9455 |
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| 0.2641 | 5.9029 | 760 | 0.1931 | 0.9439 | 0.9414 | 0.9466 | 0.9421 |
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| 0.2391 | 5.9806 | 770 | 0.1925 | 0.9439 | 0.9414 | 0.9460 | 0.9421 |
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| 0.2601 | 6.0583 | 780 | 0.1922 | 0.9466 | 0.9446 | 0.9485 | 0.9450 |
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| 0.2499 | 6.1359 | 790 | 0.1921 | 0.9461 | 0.9443 | 0.9480 | 0.9443 |
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| 0.264 | 6.2136 | 800 | 0.1877 | 0.9466 | 0.9450 | 0.9479 | 0.9451 |
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| 0.2523 | 6.2913 | 810 | 0.1875 | 0.9468 | 0.9453 | 0.9483 | 0.9455 |
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| 0.2406 | 6.3689 | 820 | 0.1880 | 0.9495 | 0.9477 | 0.9516 | 0.9481 |
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| 0.2749 | 6.4466 | 830 | 0.1885 | 0.9466 | 0.9448 | 0.9483 | 0.9451 |
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| 0.2702 | 6.5243 | 840 | 0.1885 | 0.9468 | 0.9451 | 0.9482 | 0.9455 |
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| 0.2482 | 6.6019 | 850 | 0.1863 | 0.9475 | 0.9461 | 0.9493 | 0.9464 |
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| 0.2403 | 6.6796 | 860 | 0.1897 | 0.9470 | 0.9451 | 0.9497 | 0.9453 |
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| 0.2509 | 6.7573 | 870 | 0.1906 | 0.9483 | 0.9462 | 0.9508 | 0.9465 |
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| 0.2689 | 6.8350 | 880 | 0.1867 | 0.9485 | 0.9459 | 0.9506 | 0.9466 |
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| 0.2159 | 6.9126 | 890 | 0.1866 | 0.9485 | 0.9464 | 0.9504 | 0.9468 |
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| 0.2488 | 6.9903 | 900 | 0.1866 | 0.9461 | 0.9435 | 0.9482 | 0.9443 |
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| 0.2366 | 7.0680 | 910 | 0.1871 | 0.9448 | 0.9422 | 0.9464 | 0.9430 |
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| 0.2602 | 7.1456 | 920 | 0.1854 | 0.9466 | 0.9441 | 0.9483 | 0.9447 |
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| 0.2236 | 7.2233 | 930 | 0.1859 | 0.9453 | 0.9429 | 0.9467 | 0.9436 |
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| 0.2463 | 7.3010 | 940 | 0.1863 | 0.9470 | 0.9450 | 0.9488 | 0.9454 |
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| 0.2355 | 7.3786 | 950 | 0.1862 | 0.9461 | 0.9437 | 0.9476 | 0.9443 |
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| 0.263 | 7.4563 | 960 | 0.1860 | 0.9473 | 0.9453 | 0.9491 | 0.9456 |
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| 0.2384 | 7.5340 | 970 | 0.1860 | 0.9473 | 0.9453 | 0.9492 | 0.9456 |
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| 0.2229 | 7.6117 | 980 | 0.1856 | 0.9478 | 0.9458 | 0.9497 | 0.9461 |
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| 0.2277 | 7.6893 | 990 | 0.1855 | 0.9480 | 0.9460 | 0.9499 | 0.9463 |
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| 0.2485 | 7.7670 | 1000 | 0.1854 | 0.9480 | 0.9460 | 0.9499 | 0.9463 |
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### Framework versions
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model.safetensors
CHANGED
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version https://git-lfs.github.com/spec/v1
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size 348129336
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train.ipynb
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training_args.bin
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@@ -1,3 +1,3 @@
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| 1 |
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| 2 |
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